Public and Private Information in International Crises: Diplomatic Correspondence and Conflict Anticipation

Abstract

Scholars of international conflicts have long emphasized the role of private information in the onset of interstate wars. Yet, the literature lacks direct and systematic evidence of its effect. This is largely due to challenges with accessing decision-makers’ private and often confidential information and opinions. We compile a large corpus of declassified French diplomatic cables that span the period 1871–1914. Using these texts, we estimate a dynamic topic model to generate diplomatic thematic variables, which are then used to forecast the onset of French Militarized Interstate Disputes (MIDs). The inclusion of information from diplomatic correspondence greatly improves estimates of MID timing, compared to models that rely solely on public information such as structural determinants and revealed risk perceptions derived from financial markets or the press. These results emphasize the importance of private information in decisions to go to war and the limitations of empirical work that relies solely on publicly available data.

Introducing an Interpretable Deep Learning Approach to Domain-Specific Dictionary Creation: A Use Case for Conflict Prediction

“…Mueller and Rauh (2018, 2022a,b) were able to show that including features extracted from newspaper articles through a topic modeling approach can support conflict prediction models. Relatedly, Boussalis et al (2022) apply a topic modeling approach to French diplomatic cables to predict French interstate conflicts. There have also been attempts to leverage sentiment analysis.…”